Run customized LLM models on your system privately | Use ChatGPT like interface | Build local applications using Python
What you will learn
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Install and configure Ollama on your local system to run large language models privately.
Customize LLM models to suit specific needs using Ollamaβs options and command-line tools.
Execute all terminal commands necessary to control, monitor, and troubleshoot Ollama models
Set up and manage a ChatGPT-like interface using Open WebUI, allowing you to interact with models locally
Deploy Docker and Open WebUI for running, customizing, and sharing LLM models in a private environment.
Utilize different model types, including text, vision, and code-generating models, for various applications.
Create custom LLM models from a gguf file and integrate them into your applications.
Build Python applications that interface with Ollama models using its native library and OpenAI API compatibility.
Develop a RAG (Retrieval-Augmented Generation) application by integrating Ollama models with LangChain.
Implement tools and agents to enhance model interactions in both Open WebUI and LangChain environments for advanced workflows.
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